Figure 1D.1 – The Poisson distributions capture how the statistical nature of nuclear counting measurements influences the precision of such measurements. This leads naturally to the concept of mean free path. The mean free path, λ, is the thickness of formation that will reduce a beam of radiation to 1/e (approximately 37%) of its original value. It depends on the amount of material in the formation and its cross section.
Martini, Brigette (Corescan Inc.) | Bellian, Jerome (Whiting Petroleum Corporation) | Katz, David (Encana Corporation) | Fonteneau, Lionel (Corescan Pty Ltd) | Carey, Ronell (Corescan Pty Ltd) | Guisinger, Mary (Whiting Petroleum Corporation) | Nordeng, Stephan H. (University of North Dakota)
Hyperspectral core imaging studies of the Bakken-Three Forks formations over the past four years has revealed non-destructive, high resolution, spatially relevant insight into mineralogy, both primary and diagenetically altered that can be applied to reservoir characterization. While ‘big’ data like co-acquired hyperspectral imagery, digital photography and laser profiles can be challenging to analyze, synthesize, scale, visualize and store, their value in providing mineralogical information, structural variables and visual context at scales that lie between (and ultimately link) nano and reservoir-scale measurements of the Bakken-Three Forks system, is unique.
Simultaneous, co-acquired hyperspectral core imaging data (at 500 μm spatial resolution), digital color photography (at 50 μm spatial resolution) and laser profiles (at 20 μm spatial and 7 μm vertical resolution), were acquired over 24 wells for a total of 2,870 ft. of core, seven wells of which targeted the Bakken-Three Forks formations. These Bakken-Three Forks data (~5.5 TB) represent roughly 175,000,000 pixels of spatially referenced mineralogical data. Measurements were performed at a mobile Corescan HCI-3 laboratory based in Denver, CO, while spectral and spatial analysis of the data was completed using proprietary in-house spectral software, offsite in Perth, WA, Australia. Synthesis of the spectral-based mineral maps and laser-based structural data, with ancillary data (including Qemscan, XRD and various downhole geophysical surveys) were completed in several software and modelling platforms.
The resulting spatial context of this hyperspectral imaging-based mineralogy and assemblages are particularly compelling, both in small scale micro-distribution as well as borehole scale mineralogical distributions related to both primary lithology and secondary alteration. These studies also present some of the first successful measurement and derivation of lithology from hyperspectral data. Relationships between hyperspectral-derived mineralogy and oil concentrations are presented as are separately derived structural variables. The relationship between hyperspectral-based mineralogy to micro-scale reservoir characteristics (including those derived from Qemscan) were studied, as were relationships to larger-scale downhole geophysical data (resulting in compelling correlations between variables of resistivity and hyperspectral-mineralogy). Finally, basic Net-to-Gross calculations were completed using the hyperspectral imaging data, thereby extending the use of such data from geological characterizations through to resource estimations.
The high-fidelity mineralogical maps afforded by hyperspectral core imaging have not only provided new geological insight into the Bakken-Three Forks formations, but ultimately provide improved well completion designs in those formations, as well as a framework for applying the technology to other important unconventional reservoir formations in exploration and development. The semi-automated nature of the technology also ushers in the ability to consistently and accurately log mineralogy from multiple wells and fields globally, allowing for advanced comparative analysis.
A novel higher resolution spectral volume method coupled with a control-volume distributed multi-Point flux approximation (CVD-MPFA) is presented on unstructured triangular grids for subsurface reservoir simulation. The flow equations involve an essentially hyperbolic convection equation coupled with an elliptic pressure equation resulting from Darcy's law together with mass conservation. The spectral volume (SV) method is a locally conservative, efficient high-order finite volume method for convective flow. In 2D geometry, the triangular cell is subdivided into sub-cells, and the average state variables in the sub-cells are used to reconstruct a high-order polynomial in the triangular cell. The focus here is on an efficient strategy for reconstruction of both a higher resolution approximation of the convective transport flux and Darcy-flux approximation on sub-cell interfaces. The strategy involves coupling of the SV method and reconstructed CVD-MPFA fluxes at the faces of the spectral volume, to obtain an efficient finer scale higher resolution finite-volume method which solves for both the saturation and pressure. A limiting procedure based on the Barth-Jespersen limiter is used to prevent non-physical oscillations on unstructured grids. The fine scale saturation/concentration field is then updated via the reconstructed finite volume approximation over the sub-cell control-volumes. The method is also coupled with a discrete fracture model. Performance comparisons are presented for tracer and two phase flow problems on 2D unstructured meshes including fractures. The results demonstrate that the spectral-volume method achieves further enhanced resolution of flow and fronts in addition to that of achieved by the standard higher resolution method over first order upwind, while improving upon efficiency.
Embedded Discrete-Fracture Model (EDFM) is designed to accurately represent realistic hydraulic fracture network (HFN) and provide efficient performance predictions by honoring the fracture topology. Due to the complexity of HFN, the EDFM grid may be computationally inefficient, particularly for field-scale applications with millions of fracture cells. This paper aims at incorporating the Fast Marching Method (FMM) and spectral clustering for fast HFN analysis, simplification and simulation under the framework of EDFM.
HFNs are first generated using a commercial hydraulic fracture simulator. The FMM is used to solve the pressure front propagation using the fracture graph and subsequently the ‘diffusive time of flight’, well and completion index are calculated. The results are used as pre-conditions to split the fracture graph into connected components, which are subsequently partitioned using spectral clustering. The resulting clusters are used for fracture simplification resulting in a significantly lower number of fracture elements for flow simulation. To demonstrate the feasibility of the workflow, we use the Multi-Well Pad pilot model, which is characterized by a complex HFN and a high-resolution matrix system. We investigate the relationship between matrix resolution (characterized by the matrix-fracture size of the reservoir cells) and the ratio of oil and gas production on the field. Our investigation provides an alternative approach to explain the very large Gas Oil Ratio (GOR) reported for this type of reservoirs. The required levels of refinement to correctly represent the observed GOR presents an opportunity to test the efficiency and accuracy of our proposed workflow for HFN simplification. We use the results of the FMM applied to the high-resolution models to find an optimal spectral fracture clustering. The results show that the proposed workflow can achieve massive fracture cells aggregation (with only 1% of the original fracture cell number) while maintaining the accuracy.
This is the first study for analysis, simplification, and simulation of HFN for EDFM using a field scale model. The main contributions are: (i) honor the topology of complex HFNs in EDFM and is able to represent the complex physics observed in the oil and gas shale reservoirs, (ii) HFNs diagnosis without simulation, and (iii) massive fracture aggregation with an error below 5 percent, and speed-up higher than 16 times of the fine scale model.
This paper discusses the enhanced use of noise logging aimed at characterizing the dynamics of complex reservoirs and addressing wellbore integrity issues. The methodology makes use of a fit-for-purpose quantitative spectral analysis of noise log measurements and can provide direct and fast information about well completion integrity, post-stimulation job efficiency, fluid flow path in the near wellbore region, reservoir porosity characteristics and flow-units identification.
The approach is presented by means of a study performed on several wells intercepting different heterogeneous reservoirs and characterized by complex completions and, sometimes, by intensive stimulation jobs. In details, a high-resolution noise pattern modeling in a wide frequency range is performed to discriminate the character of the recorded flow noise in terms of mesopores, macropores, fractures, behind-casing channels and completion elements (including active valves and leaking packers). In favorable scenarios, the noise power amplitude is also used to understand the contribution of active reservoir units.
It is proven that providing a quantitative noise pattern classification is fundamental to recognize unusual poor cement placement issues, not detectable by standard sonic and ultrasonic cement logs and to discriminate between leaking and sealing packers. Moreover, in case of acid and/or acid fracturing treatments in carbonate reservoirs, the methodology can identify the generated wormholes/fractures and quantitatively evaluate the efficiency of the stimulation jobs by means of noise power analysis in the related frequency range. In addition, a dedicated spectral noise modeling is also used in order to identify flow-unit contributions in multi-layer scenarios and the type of porosity providing the flow. The reliability of the workflow comes after a successful comparison with the available standard production logging interpretations. The integration of this approach with standard workflows completes the reservoir characterization providing additional dynamic outcomes.
The key role played by the enhanced modeling of spectral noise log data demonstrates the versatility of the methodology. Although the added values of this logging technique are already known, the quantitative use of noise power amplitude in selected frequency ranges is relatively new and can shed light on this topic for future advanced applications.
Remote Sensing Imagery and the derived ancillary products improved the efficiency and safety of upstream oil and gas operations on the North Slope of Alaska. These Arctic regions are remote, very difficult to access in general and sometimes only seasonably accessible. Our prudent and responsible Arctic Operations require regional-reconnaissance exploration, diligent monitoring of environment such as current state of vegetation, temporal changes of terrain, water drainage system and lakes. Finally, we also need very detailed logistical-planning of field operations. Remote sensing imagery and its derived ancillary products demonstrably improved all these aspects of our Arctic Operations.
For Arctic Operations, remote sensing data consisted of optical satellite and aerial imagery at various spectral and spatial resolutions, high resolution LIDAR data for digital elevation and digital surface models and synthetic aperture radar imagery (SAR). A combination of in-house and commercial software was used to ingest and process these data. The optical imagery was processed and enhanced using various spectral combinations and high pass filtering to generate the highest possible spatial-resolution for each sensor. Classic neural networks analysis was used to classify the optical imagery for vegetation. The SAR imagery was calibrated (for all polarizations) and geometrically corrected to remove layover effects. The processed optical and SAR imagery, LIDAR and ancillary products were co-registered and imported into a GIS system for final analysis and applications.
The optical imagery provided information about surface feature such as lake outlines, general drainage, active channels in Colville River, general lake ice conditions, classification of vegetation types etc. The LIDAR data were used to generate slope maps (for arctic vehicles), general topographic conditions and field operations. The SAR imagery was used to monitor surface conditions when optical imagery was not available during the Arctic night conditions. SAR imagery was also used to calculate the ice thickness proxy maps for eventual field operations. All of these products contributed directly to our environmental baseline studies, improved our field operation efficiency and general safety of our Arctic Operations.
For a practicing engineer (individual or team) The remote sensing data and derived products for Arctic Operations were made available via GIS system. This allowed easy integration with other data layers as well as a common background for all different disciplines to monitor progress and to contribute their learnings and ideas to the entire team.
Gong, Weili (China University of Mining and Technology Beijing) | Bao, Min (China University of Mining and Technology Beijing) | Gao, Xia (China University of Mining and Technology Beijing) | Sousa, Luis (China University of Mining and Technology Beijing)
Acoustic emission (AE)/micro-seismicity (MS) signal obtained in laboratory or in-situ monitoring of the dynamic events is generally contaminated by various noises which result in strong spectral dispersion. Therefore, a precise locating of the main spectral components in conventional power/Fourier spectrum and deeper insights into the time dynamics carried by waveforms of the AE/MS signal would be a very hash work. The present research developed a-state-of-the-art technique for extracting precisely forewarning precursors in the spectral and time regimes from the noise-contained signal based on singular spectrum analysis (SSA). The extracted precursors, known as “eigenfrequency (EF)” and principal component waves are verified in a laboratory rock burst experiment and proved being robust in characterizing time dynamics and the frequency shift phenomenon consistently. The proposed AE/MS signal reduction approach and results have a potential being applied in the practical seismic event forecasting.
Acoustic emission (AE)/micro-seismicity (MS) signal, observed as time series in rock bursts carries considerable information regarding the seismic location, failure mechanisms and the constitutive behavior of the rock materials, providing a window into crack initiation and propagation till macroscopic failure (He et al., 2010). Major problem encountered for analyzing the AE/MS data lies in difficulties in extracting the inherent precursory wave components for spectral and time-dynamic analyses because of the complexity of the rock mass structure and boundary conditions.
Investigations have been carried out on rock burst forecasting based on extracting the spectral and time domain precursors from the AE signal. However, time-domain analyses were rarely seen on the internationally published academic journals as a result of the noise-containing nature of AE/MS signal. For spectral domain analyses, most efforts were made on extracting the main frequency which is the highest peak in the spectrum indicating a significant dynamic event (Michlmayr et al., 2012). Main frequencies will move from high-spectral band to the low band, termed “frequency shift”, as the rock was brought to failure such as rock bursts (Cai et al.,2001; 2007; Young et al., 2004; Michlmayr et al., 2012) and earthquakes (Lei and Satoh, 2007). Due to the complexity of the rock structure and boundary, the frequency shift phenomenon does not always hold in the above-reviewed traditional spectral analysis. As the in-situ investigations revealed that the MS events are hybrid close to the monitored outbursts and low frequency components are only the necessary condition other than the sufficient condition for forecasting the onset of the dynamics events (Priestley, 1981; Lu et al., 2012).
Shin, Seungwook (Center for Gravity, Electrical, and Magnetic Studies, Colorado School of Mines, Golden, Colorado) | Li, Yaoguo (Center for Gravity, Electrical, and Magnetic Studies, Colorado School of Mines, Golden, Colorado) | Park, Samgyu (Korea Institute Geoscience and Mineral Resource (KIGAM), Daejeon, South Korea) | Cho, Seong-Jun (Korea Institute Geoscience and Mineral Resource (KIGAM), Daejeon, South Korea)
Summary It has long been that we may be able to differentiate between different lithology units from the dependence of induced polarization (IP) signal on the mineral composition and pore space distributions. The commonly used, knowledge-driven approach based on equivalent circuit analyses, however, tends to be challenged with severe ambiguities. To overcome this difficulty, we experiment with supervised machine learning based on data-driven approach. We present a study on the feasibility of using artificial neural networks to predict lithology from spectral IP data. We show that reliable results can be obtained when the algorithm is trained to obtain a stable set of weights by applying regularization or by terminating the training based on a cross-validation criterion.
Summary Seismic attributes are powerful tools that allow interpreters to make a more comprehensive and precise seismic interpretation. In this paper, we apply an unsupervised multiattribute technique called Independent Component Analysis to reduce dimensionality and extract the most valuable information of multiple spectral magnitude components in order to make an unsupervised seismic facies classification of channel complexes located in the Moki A Formation, Taranaki Basin, New Zealand. Introduction Depending on the seismic attribute that we choose, different information can be extracted (Infante-Paez and Marfurt, 2017) from the seismic volume, thus, relying on only one attribute information lead to an incomplete seismic interpretation. For this reason, multi-attribute techniques such as Principal Component Analysis (PCA), Selforganizing Maps (SOM) are commonly used. Based on higher order statistics, Independent Component Analysis separates a multivariate signal into subcomponents which are independent of each other (Hyva rinen and Oja, 2000), thus extracting more valuable information than techniques such as Principal Component Analysis (PCA) which tends to mix geology.
Analyzing the time-frequency features of seismic traces plays an important role in seismic stratigraphy analysis and hydrocarbon detection. The current popular time-spectrum analysis methods include Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), S-transform (ST) and Matching Pursuit (MP), among which MP is the most tolerant of window/scalar effect. However, MP algorithms do not consider the interfering effects of seismic events on the estimation of optimal wavelets in each decomposition iteration. The interfered reflection seismic events may result in inaccurate estimation of optimal wavelets during the whole decomposition procedure. In this study, we propose a new matching pursuit algorithm to minimize the effect of event interfering on the estimation of optimal wavelets. The algorithm proposed here assumes that the features of wavelet remain constant in a user-defined small time window. The algorithm begins with identifying the strongest reflection. Next, we estimate the optimal wavelet and the corresponding scalar set by using a least-square approach. Then we subtract the seismic traces by the waveform computed from optimal wavelet and scalar. This procedure is repeated until the total energy of seismic traces falls below a user defined value. After the forward MP, a backward algorithm is developed to replace wrongly selected wavelets generated using the forward MP by better ones. The effectiveness of the algorithm is demonstrated by first applying it to synthetic model and then to a real seismic data set.
Presentation Date: Tuesday, October 16, 2018
Start Time: 1:50:00 PM
Location: Poster Station 14
Presentation Type: Poster